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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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base_model: |
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- Qwen/Qwen2-VL-2B-Instruct |
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--- |
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# ModelInfo |
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该模型是在Qwen2-VL-2B-Instruct模型的基础上，使用是开源数据中挖掘合成的工业领域多模态数据，经过微调所得，该模型在基本保持通用指标的基础上，有效提升了工业领域的相关指标。 |
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## HOW TO USE |
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模型的使用方案与Qwen2-VL-2B-Instruct一致：https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct#quickstart |
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``` |
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# pip install qwen-vl-utils |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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# default: Load the model on the available device(s) |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" |
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) |
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. |
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# model = Qwen2VLForConditionalGeneration.from_pretrained( |
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# "Qwen/Qwen2-VL-2B-Instruct", |
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# torch_dtype=torch.bfloat16, |
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# attn_implementation="flash_attention_2", |
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# device_map="auto", |
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# ) |
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# default processer |
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") |
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# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage. |
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# min_pixels = 256*28*28 |
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# max_pixels = 1280*28*28 |
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# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Describe this image."}, |
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], |
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} |
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] |
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# Preparation for inference |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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# Inference: Generation of the output |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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## NOTE |
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当前版本为一个实验版本，后续版本迭代进行中 |
